Performance Engineering and Cost Optimization Questions

Engineering practices and trade offs for meeting performance objectives while controlling operational cost. Topics include setting latency and throughput targets and latency budgets; benchmarking profiling and tuning across application database and infrastructure layers; memory compute serialization and batching optimizations; asynchronous processing and workload shaping; capacity estimation and right sizing for compute and storage to reduce cost; understanding cost drivers in cloud environments including network egress and storage tiering; trade offs between real time and batch processing; and monitoring to detect and prevent performance regressions. Candidates should describe measurement driven approaches to optimization and be able to justify trade offs between cost complexity and user experience.

MediumTechnical

0 practiced

You are part of an on-call rotation and receive a page indicating increased error budget burn and rising p99 latency. Describe the first 10 actions you would take, prioritized, to stabilize the system, mitigate customer impact, and prepare for a post-incident review.

MediumSystem Design

0 practiced

Design a canary deployment strategy for a latency-sensitive service where tail latency matters. Explain traffic weights, metrics for success (including p99), warmup behaviors, and automated rollback triggers. How would you avoid false positives due to natural traffic variability?

HardTechnical

0 practiced

Tail latency is hurting 0.5% of users due to queuing and head-of-line blocking in a downstream service. Propose system-level changes to reduce p99: hedged requests, priority queues, admission control, timeouts, and client-side fallbacks. For each change, explain expected impact on latency, throughput, and cost.

MediumTechnical

0 practiced

Given this SQL query that scans a large orders table, recommend concrete indexing, read-replica, or caching strategies to reduce p99 query latency. Include how you'd measure and validate the improvement.

Query:

SELECT user_id, SUM(amount)
FROM orders
WHERE created_at >= date_trunc('month', now())
GROUP BY user_id
ORDER BY SUM(amount) DESC
LIMIT 100

MediumSystem Design

0 practiced

Design a safe gradual rollout strategy to enable gzip compression on API responses for a large user base. Consider how to A/B test performance, measure CPU overhead versus network egress savings, detect regressions, and provide rollback criteria.

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